20.500.12556/DKUM-94388 Predicting wine quality under changing climate An integrated approach combining machine learning, statistical analysis, and systems thinking Climate change poses significant challenges for viticulture, particularly in regions known for producing high-quality wines. Wine quality results from a complex interaction between climatic factors, regional characteristics, and viticultural practices. Methods: This study integrates statistical analysis, machine learning (ML) algorithms, and systems thinking to assess the extent to which wine quality can be predicted using monthly weather data and regional classification. The dataset includes average wine scores, monthly temperatures and precipitation, and categorical region data for Slovenia between 2011 and 2021. Predictive models tested include Random Forest, Support Vector Machine, Decision Tree, and linear regression. In addition, Causal Loop Diagrams (CLDs) were constructed to explore feedback mechanisms and systemic dynamics. Results: The Random Forest model showed the highest prediction accuracy (R2 = 0.779). Regional classification emerged as the most influential variable, followed by temperatures in September and April. Precipitation did not have a statistically significant effect on wine ratings. CLD models revealed time delays in the effects of adaptation measures and highlighted the role of perceptual lags in growers’ responses to climate signals. Conclusions: The combined use of ML, statistical methods, and CLDs enhances understanding of how climate variability influences wine quality. This integrated approach offers practical insights for winegrowers, policymakers, and regional planners aiming to develop climate-resilient viticultural strategies. Future research should include phenological phase modeling and dynamic simulation to further improve predictive accuracy and system-level understanding. wine quality machine learning climate change viticulture Slovenia terroir statistical analysis causal loop diagrams system thinking kakovost vina strojno učenje podnebne spremembe vinogradništvo Slovenija statistična analiza diagrami vzročne zanke sistemsko razmišljanje true true true MDPI Angleški jezik Slovenski jezik Znanstveno delo 2025-08-15 03:21:51 2025-08-18 09:54:04 2025-11-18 15:07:14 0000-00-00 00:00:00 2025 0 0 Str. 1-27 št. 4, št. članka 116 Letn. 11 2025 0000-00-00 Zaloznikova Objavljeno NiDoloceno 2025-06-26 2025-08-05 2025-08-11 634.8:551.583:004.85 245648387 10.3390/beverages11040116 2306-5710 Borlinic_2025_Predicting_Wine_Quality.pdf Borlinic_2025_Predicting_Wine_Quality.pdf 1 61F91BA8C11D038150969F7635C6B56F 9b09492a194d39180707dfe0e5e059c43bedd666d6c0184f7a6f1700e5593c86 6820316d-7c08-11f0-b405-00155d000105 https://dk.um.si/Dokument.php?lang=slv&id=195423 https://doi.org/10.3390/beverages11040116 0 38ec0bfb-7976-11f0-8d64-00155d000105 https://dk.um.si/Dokument.php?lang=slv&id=195364 Fakulteta za turizem Fakulteta za organizacijske vede Fakulteta za kmetijstvo in biosistemske vede 0 0 0